Looks like a human head, but it is obviously missing certain parts. Of course, this would be a model, and it could be considered a scientific model, as it represents the anatomy of the head and skull. It can obviously be used to teach about this anatomy.

To describe particular parts of a phenomenon, or the interactions among a set of phenomena, it is sometimes helpful to develop a model of the phenomenon.
Scientific models
are representations of reality. They can be a physical, mathematical, or logical representation of a system, phenomenon, or process, and they allow scientists to investigate a phenomenon in a controlled way. For instance, a scale model of a house or of a solar system is clearly not an actual house or an actual solar system; the parts of an actual house or an actual solar system represented by a scale model are, only in limited ways, representative of the actual objects (
Figure
below
).

A model of planets of the solar system. This model is clearly not a real solar system; it is a representation of the planets Mercury, Venus, Earth, Mars, Jupiter, Saturn, and Uranus. Scientists use representations of natural things to learn more about them. Also, the visitors to the Griffith Observatory in Los Angeles, California can get a better idea of the relative sizes of the planets by observing this model.

Scientific modeling
is the process of making abstract models of natural phenomena. An
abstract model
is a theoretical construct that represents something. Models are developed to allow reasoning within a simplified framework that is similar to the phenomena being investigated. The simplified model may assume certain things that are known to be incomplete in some details. Such assumptions can be useful in that they simplify the model, while at the same time, allowing the development of acceptably accurate solutions. These models play an important role in developing scientific theories.

A
simulation
is a model that runs over time. A simulation brings a model to life and shows how a particular object or phenomenon will behave. It is useful for testing, analysis or training where real-world systems or concepts can be represented by a model. For the scientist, a model also provides a way for calculations to be expanded to explore what might happen in different situations. This method often takes the form of models that can be programmed into computers. The scientist controls the basic assumptions about the variables in the model, and the computer runs the simulation, eventually coming to a complicated answer.

Examples of models include:

Computer models

Weather forecast models

Molecular models

Climate models

Ecosystem models

Geologic models

One of the main aims of scientific modeling is to allow researchers to quantify their observations about the world. In this way, researchers hope to see new things that may have escaped the notice of other researchers. There are many techniques that model builders use which allow us to discover things about a phenomenon that may not be obvious to everyone. The National Weather Service Enhanced Radar Images web site (
http://radar.weather.gov/
) is an excellent example of a simulation. The site exhibits current weather forecasts across the United States.

These two food chains represent the flow of energy in complex systems in nature. These conceptual models make the systems easier to understand. Models of very complex systems are often based on mathematical equations or computer simulations.

Evaluating Models

A person who develops a model must be able to recognize whether a model reflects reality. They must also be able to identify and work with differences between actual data and theory.

A model is evaluated mostly by how it reflects past observations of the phenomenon. Any model that is not consistent with reproducible observations must be modified or rejected. However, a fit to observed data alone is not enough for a model to be accepted as valid. Other factors important in evaluating a model include:

its ability to explain past observations,

its ability to predict future observations,

its ability to control events,

the cost of its use, especially when used with other models,

ease of use and how it looks.

Some examples of the different types of models that are used by science are shown in
Figures
below
and
below
.

A computer model of wind patterns across the continental United States for 19 November, 2007. This model is used to forecast wind speeds and directions. Data on wind speed, direction, and related data are entered into a computer which then produces this simulation. This visual model is much easier for a person to understand than a large table of numbers.

Biosphere 2 is an example of a very large three-dimensional model which biologists built to attempt to recreate a self-sustaining biome. To learn more about biomes and ecosystems, see
Ecology - Advanced
.

Theories as Models

Scientific theories are constructed in order to explain, predict and understand phenomena. For example, this could include the movement of planets, weather patterns, or the behavior of animals. In many instances we are constructing models of reality. A theory makes generalizations about observations and is made up of a related set of ideas and models. The important difference between theories and models is that the first is explanatory as well as descriptive, while the second is only descriptive and predictive in a much more limited sense.

Model Organisms

A
model organism
is a non-human species that is extensively studied to understand particular biological processes and concepts. These organisms are chosen because it is believed that discoveries made in the model organism will provide insight into the workings of other organisms, including humans. Model organisms range from single-celled bacteria to complex multi-cellular organisms. Even some viruses are utilized as models, though technically a virus is not considered an organism.

The
Table
below
lists some common model organisms. All of these organisms listed have had their complete genomes sequenced.

Common Model Organisms

Organism

Common Name

Prokaryote

Escherichia coli

E. coli
bacteria

Eukaryote, unicellular

Saccharomyces cerevisiae

Yeast

Eukaryote, multicellular

Neurospora crassa

Caenorhabditis elegans

Drosophila melanogaster

Arabidopsis thaliana

bread mold

nematode

fruit fly

thale cress

Vertebrate

Danio rerio

Mus musculus

Xenopus laevis

Macaca mulatta

zebrafish

house mouse

African clawed frog

rhesus monkey

Vocabulary

abstract model
: A theoretical construct that represents something.

model organism
: A non-human species that is extensively studied to understand particular biological phenomena.

scientific model
: A physical, mathematical, or logical representation of a system, phenomenon, or process; allow scientists to investigate a phenomenon in a controlled way.

scientific modeling
: The process of making abstract models of natural phenomena.

simulation
: A model that runs over time.

Summary

Scientific models are representations of reality. They can be a physical, mathematical, or logical representation of a system, phenomenon, or process, and they allow scientists to investigate a phenomenon in a controlled way.